Polysome profiling v-abl preB cells – WT and G3Bp1 KO, two runs

library(readxl)
library(ggplot2)
library(tidyverse)
library(patchwork)

df <- read_excel("~/Library/CloudStorage/OneDrive-BABRAHAM/experiments/OOPS_AB024/data_combined_long.xlsx")
colnames(df)[1] <- "Position"

## Add metadata
df <- df %>% 
  mutate(Treatment = case_when(
    str_detect(Sample, "im") ~ "imatinib 5h",
    TRUE ~ "untreated"
  ))

df <- df %>% 
  mutate(Cell_line = case_when(
    str_detect(Sample, "A6") ~ "G3Bp1 KO A6",
    str_detect(Sample, "WT") ~ "WT",
    str_detect(Sample, "Wt") ~ "WT",
    str_detect(Sample, "B10") ~ "G3Bp1 KO B10",
    TRUE ~ "ERROR"
  ))

df

Count readings for sanity check

df |> group_by(Treatment) |> count()

df |> group_by(Cell_line) |> count()

df |> group_by(Sample) |> count()

df |> group_by(Replication) |> count()

Plot two runs side by side (before normalisation)

Normalise the position against the 60S peak of WT untreated

df$Normalised_position <- df$Position
df$Normalised_absorbance <- df$Absorbance

#run1
df$Normalised_position[df$Sample == "8A6" & df$Replication == 1] <- df$Position[df$Sample == "8A6" & df$Replication == 1] + 0.5
df$Normalised_position[df$Sample == "8A6im" & df$Replication == 1] <- df$Position[df$Sample == "8A6im" & df$Replication == 1] - 1.75
df$Normalised_position[df$Sample == "WTim" & df$Replication == 1] <- df$Position[df$Sample == "WTim" & df$Replication == 1] - 1.25
df$Normalised_position[df$Sample == "8B10" & df$Replication == 1] <- df$Position[df$Sample == "8B10" & df$Replication == 1] - 1.5
df$Normalised_position[df$Sample == "8B10im" & df$Replication == 1] <- df$Position[df$Sample == "8B10im" & df$Replication == 1] - 0.5
df$Normalised_absorbance[df$Sample == "8A6im" & df$Replication == 1] <- df$Absorbance[df$Sample == "8A6im" & df$Replication == 1] - 0.05
df$Normalised_absorbance[df$Sample == "8A6" & df$Replication == 1] <- df$Absorbance[df$Sample == "8A6" & df$Replication == 1] - 0.03

#run2
df$Normalised_position[df$Sample == "8A6" & df$Replication == 2] <- df$Position[df$Sample == "8A6" & df$Replication == 2] - 1.5
df$Normalised_position[df$Sample == "8A6im" & df$Replication == 2] <- df$Position[df$Sample == "8A6im" & df$Replication == 2] - 0.25
df$Normalised_position[df$Sample == "WTim" & df$Replication == 2] <- df$Position[df$Sample == "WTim" & df$Replication == 2] - 1.5
df$Normalised_position[df$Sample == "8B10" & df$Replication == 2] <- df$Position[df$Sample == "8B10" & df$Replication == 2] - 1.5
df$Normalised_position[df$Sample == "8B10im" & df$Replication == 2] <- df$Position[df$Sample == "8B10im" & df$Replication == 2] - 0.25
df$Normalised_absorbance[df$Sample == "WT" & df$Replication == 2] <- df$Absorbance[df$Sample == "WT" & df$Replication == 2] + 0.03

Plot two runs side by side (normalised)

Plot the same data but focus on polysomes

Plotting untreated vs imatinib treated samples by cell line

Made this graph by mistake but liked how the area between lines is highlighted.